Body fat, pericardial fat, liver fat and arterial health at age 10 years

Summary Background Body mass index is associated with carotid intima‐media thickness and distensibility in adults and children. Objective To examine whether general and specific fat depots are associated with these markers of arterial health at school age. Methods This cross‐sectional analysis was embedded in a population‐based prospective cohort study among 4708 children aged 10 years. Body, lean and fat mass index were estimated by dual‐energy X‐ray absorptiometry. Pericardial, visceral and liver fat were estimated by magnetic resonance imaging. Carotid intima‐media thickness and distensibility were measured by ultrasound. Results A 1‐standard‐deviation‐score (SDS) higher body mass index was associated with higher carotid intima‐media thickness (0.06 SDS, 95% confidence interval [CI]: 0.03–0.08) and lower distensibility (−0.17 SDS, 95% CI: −0.20 to −0.14). These associations tended to be similar for lean mass index. A 1‐SDS higher fat mass index was associated with lower carotid intima‐media thickness (−0.08 SDS, 95% CI: −0.11 to −0.05) and lower distensibility (−0.10 SDS, 95% CI: −0.14 to −0.07). A 1‐SDS higher liver fat fraction was associated with lower carotid intima‐media thickness (−0.04 SDS, 95% CI: −0.08 to −0.00) and lower distensibility (−0.06 SDS, 95% CI: −0.10 to −0.03). We observed similar associations for visceral fat. Conclusions At school age, lean and fat mass seem to be differentially related to carotid intima‐media thickness but not distensibility. Arterial development might be affected by lean mass, general and specific fat mass.


Assessment of pericardial fat, visceral fat and liver fat
At age 10 years, we obtained visceral, pericardial and liver fat from magnetic resonance imaging (MRI) scans. Children were scanned using a 3.0 Tesla MRI (Discovery MR 750w, GE Healthcare, Milwaukee, WI, USA). Pericardial fat imaging in short axis orientation was performed using an electrocardiogram-triggered black-blood-prepared thin slice single shot fast spin echo acquisition with multi-breath-hold approach. An axial 3-point Dixon acquisition for fat and water separation (IDEAL IQ) was used for liver fat imaging. 1 An axial abdominal scan from lower liver to pelvis and a coronal scan centered at the head of the femurs were performed with a 2-point DIXON acquisition (LavaFlex). The obtained fat scans were subsequently analyzed by the Precision Image Analysis company (PIA, Kirkland, WA, USA), using the sliceOmatic (TomoVision, Magog, Canada) software package. Extraneous structures and any image artifacts were removed manually. 2 Pericardial fat included both epicardial and paracardial fat directly attached to the pericardium, ranging from the apex to the left ventricular outflow tract. Total visceral fat volume ranged from the dome of the liver to the superior part of the femoral head. Fat mass was obtained by multiplying the total volumes by the specific gravity of adipose tissue, 0.9 g/mL. Liver fat fraction was determined by taking 4 samples of at least 4 cm2 from the central portion of the hepatic volume. Subsequently, the mean signal intensities were averaged to generate overall mean liver fat fraction estimation.

Conditional regression analyses
Body mass index is only a crude measure of adiposity, reflecting the sum of lean and fat mass without distinguishing between these components. 8-11 Also, the associations of android-gynoid ratio, pericardial fat, visceral fat and liver fat with carotid intima-media thickness and carotid distensibility may not be statistically independent from lean mass index and/or fat mass index. To further examine this, we performed conditional regression modelling as sensitivity analyses. First, we built conditional models for each of the regional fat measures using linear regression analyses. In the lean mass conditional models, we obtained the standardized residual for each regional fat measure from the regression of the respective fat measure on lean mass index. These standard residuals are completely uncorrelated with lean mass index. We used the same approach for fat mass index: in the fat mass conditional models, we obtained the standardized residual for each regional fat measure from the regression of the respective fat measure on fat mass index. These standard residuals are completely uncorrelated with fat mass index. 3,4 Thus, for each regional fat measure, we constructed one new variable independent of lean mass, and one new variable independent of fat mass. Second, for each of the two independent variables per regional fat measure, we used conventional linear regression analyses (confounder models) to assess their associations with carotid intima-media thickness. The obtained results were thus independent of lean mass and fat mass, respectively. Third, in a similar manner we assessed the associations of the two standard residuals per regional fat measure with carotid distensibility, independent of lean mass and fat mass, respectively. Fourth, for each regional fat measure, we were able to compare the results that we obtained from the confounder model to the results that were independent of lean and fat mass, respectively (Table S4). Figure S1. Flow chart of the study population * The non-response analysis compared included children to the 604 children who had information on any exposure but were not included in the analysis because they had no information on common carotid artery intima-media thickness or distensibility available. 9901 Children enrolled in the Generation R study at birth \ 4293 Children had no information on body mass index and DXA and MRI measurements at age 10 years 5058 Children had information on carotid intima-media thickness and carotid distensibility at age 10 years 5662 Children with information on body mass index and body fat distribution and/or pericardial, visceral and liver fat at age 10 years \ 604 Children had no information on carotid intima-media thickness and carotid distensibility at age 10 years * 4708 Singleton children aged 10 years had information on any exposure and any outcome and were included in the analyses 4708 Common carotid artery intima-media thickness 4530 Common carotid artery distensibility 4687 Body mass index, lean mass index, fat mass index, android-gynoid ratio 2811 Pericardial fat, visceral fat, liver fat 350 Children had a sibling who was included in the analysis

Figure S2. Directed acyclic graph
Directed acyclic graph represents assumptions of a causal relationship between the exposures and outcomes. Exposures included body mass index, lean mass index, fat mass index, android-gynoid ratio, visceral fat index, pericardial fat index and liver fat fraction. The other variables are considered confounders or potential mediators.   Table S4. Associations of body mass, lean mass, fat mass, pericardial fat, visceral fat and liver fat with carotid intima-media thickness and carotid distensibility at age 10 years SDS: standard-deviation-score Regression coefficients are linear regression coefficients based on standard-deviation-scores of carotid intima-media thickness and carotid distensibility. Carotid distensibility was natural log-transformed. Models were adjusted for child sex and age at outcome measurement.
Confounder models were additionally adjusted for child ethnicity, maternal age and education. *P<0.05, †P<.01. ‡ In the mutually adjusted model we additionally adjusted the confounder model for fat mass and lean mass index, respectively. § Based on conditional regression analyses in which the respective fat measure was regressed on lean mass index to create fat measures independent of lean mass index. These models were not run for body mass index, as it is defined as the sum of lean and fat mass index. || Based on conditional regression analyses in which the respective fat measure was regressed on fat mass index to create fat measures independent of fat mass index. These models were not run for body mass index, as it is defined as the sum of lean and fat mass index.

Carotid intima-media thickness Difference (95% confidence interval) N=4708
Carotid distensibility Difference (95% confidence interval) N=4530 Body mass index, SDS (n=4697)  Table S5. Associations of body mass index, lean mass index, fat mass index and android-gynoid ratio with carotid intima-media thickness and carotid distensibility at age 10 years among children with information on all exposures and outcomes (n=2135) Carotid intima-media thickness Difference (95% confidence interval) Carotid distensibility Difference (95% confidence interval) Table S6. Associations of pericardial fat, visceral fat and liver fat with carotid intima-media thickness and carotid distensibility at age 10 years among children with information on all exposures and outcomes (n=2135) SDS: standard-deviation-score. Regression coefficients are linear regression coefficients based on standard-deviation-scores of carotid intima-media thickness and carotid distensibility. Carotid distensibility was natural log-transformed. Confounder models were adjusted for child sex and age at outcome measurement, child ethnicity, maternal age and education. *P<0.05, †P<0.01.